

@misc{45953838_0-web,
   author={Junhui Li},
   howpublished = {\url{ https://www.sohu.com/a/37167071_114774}},
}


@misc{doc-ifxrcizu4121450-web,
   author={Ying Cui},
   howpublished = {\url{ http://news.ifeng.com/c/7fbHXR4A41x}},
}

@misc{twitter-api,
	author={Twitter Developers},
    howpublished = {\url{ https://dev.twitter.com/streaming}},
}


@misc{abcnews,
    howpublished = {\url{ http://abcnews.go.com/GMA/story?id=2273111&page=1}},
}
@misc{SB115457177198425388,
     howpublished = {\url{ http://www.wsj.com/articles/SB115457177198425388}},
}
@misc{IZSqXUSwHRI,
     howpublished = {\url{ https://www.youtube.com/watch?v=IZSqXUSwHRI}},
}
@misc{V449Ifl96hc,
     howpublished = {\url{http://lubbockonline.com/stories/082401/bus_082401067.shtml#.V449Ifl96hc}},
}
@misc{wikipedia-editing-for-zionists,
     howpublished = {\url{http://thelede.blogs.nytimes.com/2010/08/20/wikipedia-editing-for-zionists/}},
}
@misc{online-astroturfing,
     howpublished = {\url{ https://corporatewatch.org/magazine/52/springsummer-2012/online-astroturfing}},
}
@misc{MeanFScore-web,
     author={Mean F Score},
     howpublished = {\url{https://www.kaggle.com/wiki/MeanFScore}},
}
@misc{50_Cent_Party,
     howpublished = {https://en.wikipedia.org/wiki/50_Cent_Party},
}
@misc{Astroturf,
     howpublished = {\url{http://rationalwiki.org/wiki/Astroturf}},
}
@misc{photostream,
     howpublished = {\url{https://www.flickr.com/photos/greensefa/8202845905/in/photostream/}},
}
@misc{election2005,
     howpublished = {\url{http://www.theguardian.com/politics/2005/may/22/uk.election2005}},
}
@article{Kelman1958Compliance,
  title={Compliance, Identification, and Internalization: Three Processes of Attitude Change},
  author={Kelman, Herbert C.},
  journal={Journal of Conflict Resolution},
  volume={2},
  number={1},
  pages={51-60},
  year={1958},
 keywords={angiogenesis;vascular endothelial growth factor;angiopoietin;diabetic retinopathy},
 abstract={No abstract is available for this item.},
}
@article{StreitfeldThe,
  title={The best reviews money can buy},
  author={Streitfeld, By David},
}
@article{Lee2010The,
  title={The Roots of Astroturfing},
  author={Lee, Caroline W.},
  journal={Contexts},
  volume={9},
  number={1},
  pages={73-75},
  year={2010},
}

@article{Mackenzie2009Lobbying,
  title={Lobbying memo splits US oil industry},
  author={Mackenzie, K.,Pickard, J},
  journal={in: Financial Times, London (UK), United Kingdom, London (UK)},
  year={2009},
}
@article{Benevenuto2010Detecting,
  title={Detecting spammers on twitter},
  author={Benevenuto,F.,Magno,G.,Rodrigues,T.,Almeida,V},
  journal={In Collaboration, electronic messaging, anti-abuse and spam conference (CEAS) },
  volume={2},
  pages={12},
  year={2010},
}
@article{Chen2013BattlingTI,
  title={Battling the Internet Water Army: Detection of Hidden Paid Posters},
  author={Cheng Chen and Kui Wu and S. Venkatesh and Xudong Zhang},
  journal={CoRR},
  year={2013},
  volume={abs/1111.4297},
}
@inproceedings{Zeng2014Behavior,
  title={Behavior Modeling of Internet Water Army in Online Forums},
  author={Zeng, Ke and Wang, Xiao and Zhang, Qingpeng and Zhang, Xinzhan and Wang, Fei Yue},
  booktitle={Ifac World Congress},
  pages={9858-9863},
  year={2014},
 abstract={The behavior patterns and strategies of Internet Water Army in online forums are investigated in this paper. Internet Water Army focuses on the controlling and steering of cyber collective opinions, and adjusts their behavior according to two principles: to avoid being exposed and to increase the ability to exert influence. To study how the ability of Internet Water Army to exert influence, we construct a multi-agent system with coevolution of topics and cyber collective behaviors and design the behavior patterns and strategies of Internet Water Army. Based on synthetic data and real data, we find that Internet Water Army dynamically adjusts their behavior strategy to maximize their influence and the effectiveness of strategy of Internet Water Army is closely related to the features of the users. Our work sheds insight on the design of viral marketing mechanism in e-commerce systems as well as on guiding collective behaviors in social media.},
}

@inproceedings{Grier2010,
  title={@spam: the underground on 140 characters or less},
  author={Grier, Chris and Thomas, Kurt and Paxson, Vern and Zhang, Michael},
  booktitle={ACM Conference on Computer and Communications Security},
  pages={27-37},
  year={2010},
 keywords={spam;twitter},
 abstract={In this work we present a characterization of spam on Twitter. We find that 8 % of 25 million URLs posted to the site point to phishing, malware, and scams listed on popular blacklists. We analyze the accounts that send spam and find evidence that it originates from previously legitimate accounts that have been compromised and are now being puppeteered by spammers. Using clickthrough data, we analyze spammers é¥ use of features unique to Twitter and the degree that they affect the success of spam. We find that Twitter is a highly successful platform for coercing users to visit spam pages, with a clickthrough rate of 0.13%, compared to much lower rates previously reported for email spam. We group spam URLs into campaigns and identify trends that uniquely distinguish phishing, malware, and spam, to gain an insight into the underlying techniques used to attract users. Given the absence of spam filtering on Twitter, we examine whether the use of URL blacklists would help to significantly stem the spread of Twitter spam. Our results indicate that blacklists are too slow at identifying new threats, allowing more than 90 % of visitors to view a page before it becomes blacklisted. We also find that even if blacklist delays were reduced, the use by spammers of URL shortening services for obfuscation negates the potential gains unless tools that use blacklists develop more sophisticated spam filtering.},
}

@article{Thomas2011Design,
  title={Design and Evaluation of a Real-Time URL Spam Filtering Service},
  author={Thomas, Kurt and Grier, Chris and Ma, Justin and Paxson, Vern and Song, Dawn},
  volume={42},
  number={12},
  pages={447-462},
  year={2011},
 keywords={Web services;information filtering;invasive software;social networking (online);unsolicited e-mail;Monarch scalability;Twitter spam;URL shorteners;email based spam filtering techniques;malware},
 abstract={On the heels of the widespread adoption of web services such as social networks and URL shorteners, scams, phishing, and malware have become regular threats. Despite extensive research, email-based spam filtering techniques generally fall short for protecting other web services. To better address this need, we present Monarch, a real-time system that crawls URLs as they are submitted to web services and determines whether the URLs direct to spam. We evaluate the viability of Monarch and the fundamental challenges that arise due to the diversity of web service spam. We show that Monarch can provide accurate, real-time protection, but that the underlying characteristics of spam do not generalize across web services. In particular, we find that spam targeting email qualitatively differs in significant ways from spam campaigns targeting Twitter. We explore the distinctions between email and Twitter spam, including the abuse of public web hosting and redirector services. Finally, we demonstrate Monarch's scalability, showing our system could protect a service such as Twitter -- which needs to process 15 million URLs/day -- for a bit under $800/day.},
}

@inproceedings{Gao2010Detecting,
  title={Detecting and characterizing social spam campaigns},
  author={Gao, Hongyu and Hu, Jun and Wilson, Christo and Li, Zhichun and Chen, Yan and Zhao, Ben Y.},
  booktitle={Proceedings of the 17th ACM conference on Computer and communications security},
  pages={681-683},
  year={2010},
 keywords={online social networks;spam;spam campaigns},
 abstract={CiteSeerX - Scientific documents that cite the following paper: Detecting and Characterizing Social Spam Campaigns},
}

@inproceedings{Yang2012Analyzing,
  title={Analyzing spammers' social networks for fun and profit: a case study of cyber criminal ecosystem on twitter},
  author={Yang, Chao and Harkreader, Robert and Zhang, Jialong and Shin, Seungwon and Gu, Guofei},
  booktitle={Proceedings of the 21st international conference on World Wide Web},
  pages={71-80},
  year={2012},
 keywords={ecosystem;online social network;spammer},
 abstract={ABSTRACT In this paper, we perform an empirical analysis of the cyber criminal ecosystem on Twitter. Essentially, through analyzing inner social relationships in the criminal account community, we find that criminal accounts tend to be socially connected, forming a small-world network. We also find that criminal hubs, sitting in the center of the social graph, are more inclined to follow criminal accounts. Through analyzing outer social relationships between criminal accounts and their social friends outside the criminal account community, we reveal three categories of accounts that have close friendships with criminal accounts. Through these analyses, we provide a novel and effective criminal account inference algorithm by exploiting criminal accounts' social relationships and semantic coordinations.},
}

@inproceedings{Sedhai2015HSpam14,
  title={HSpam14: A Collection of 14 Million Tweets for Hashtag-Oriented Spam Research},
  author={Sedhai, Surendra and Sun, Aixin},
  booktitle={The  International ACM SIGIR Conference},
  pages={223-232},
  year={2015},
 keywords={hashtag;spam;tweets;twitter},
 abstract={<p>Hashtag facilitates information diffusion in Twitter by creating dynamic and virtual communities for information aggregation from all Twitter users. Because hashtags serve as additional channels for one's tweets to be potentially accessed by other users than her own followers, hashtags are targeted for spamming purposes (e.g., hashtag hijacking), particularly the popular and trending hashtags. Although much effort has been devoted to fighting against email/web spam, limited studies are on hashtag-oriented spam in tweets. In this paper, we collected 14 million tweets that matched some trending hashtags in two months' time and then conducted systematic annotation of the tweets being spam and ham (i.e., non-spam). We name the annotated dataset HSpam14. Our annotation process includes four major steps: (i) heuristic-based selection to search for tweets that are more likely to be spam, (ii) near-duplicate cluster based annotation to firstly group similar tweets into clusters and then label the clusters, (iii) reliable ham tweets detection to label tweets that are non-spam, and (iv) Expectation-Maximization (EM)-based label prediction to predict the labels of remaining unlabeled tweets. One major contribution of this work is the creation of HSpam14 dataset, which can be used for hashtag-oriented spam research in tweets. Another contribution is the observations made from the preliminary analysis of the HSpam14 dataset.</p>},
}

@inproceedings{Hu2014LeveragingKA,
  title={Leveraging knowledge across media for spammer detection in microblogging},
  author={Xia Hu and Jiliang Tang and Huan Liu},
  booktitle={SIGIR},
  year={2014}
}
@inproceedings{Yang2015PennyFY,
  title={Penny for Your Thoughts: Searching for the 50 Cent Party on Sina Weibo},
  author={Xiaofeng Yang and Qian Yang and Christo Wilson},
  booktitle={ICWSM},
  year={2015}
}
@inproceedings{Sun2013SyntheticRS,
  title={Synthetic review spamming and defense},
  author={Huan Sun and Alex Morales and Xifeng Yan},
  booktitle={KDD},
  year={2013}
}
@inproceedings{Song2015CrowdTarget,
  title={CrowdTarget: Target-based Detection of Crowdturfing in Online Social Networks},
  author={Song, Jonghyuk and Lee, Sangho and Kim, Jong},
  booktitle={ACM Sigsac Conference on Computer and Communications Security},
  pages={111-114},
  year={2015},
 keywords={malicious crowdsourcing;online social networks;twitter;underground services},
 abstract={Malicious crowdsourcing, also known as crowdturfing, has become an important security problem. However, detecting accounts performing crowdturfing tasks is challenging because human workers manage the crowdturfing accounts such that their characteristics are similar with the characteristics of normal accounts. In this paper, we propose a novel crowdturfing detection method, called CrowdTarget, that aims to detect target objects of crowdturfing tasks (e.g., post, page, and URL) not accounts performing the tasks. We identify that the manipulation patterns of target objects by crowdturfing workers are unique features to distinguish them from normal objects. We apply CrowdTarget to detect collusion-based crowdturfing services to manipulate account popularity on Twitter with artificial retweets. Evaluation results show that CrowdTarget can accurately distinguish tweets receiving crowdturfing retweets from normal tweets. When we fix the false-positive rate at 0.01, the best true-positive rate is up to 0.98.},
}
@inproceedings{Liu2016PayMA,
  title={Pay Me and I'll Follow You: Detection of Crowdturfing Following Activities in Microblog Environment},
  author={Yuli Liu and Yiqun Liu and Min Zhang and Shaoping Ma},
  booktitle={IJCAI},
  year={2016}
}
@inproceedings{Fakhraei2015Collective,
  title={Collective Spammer Detection in Evolving Multi-Relational Social Networks},
  author={Fakhraei, Shobeir and Foulds, James and Shashanka, Madhusudana and Getoor, Lise},
  booktitle={ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  pages={1769-1778},
  year={2015},
 keywords={collective classification;graph mining;graphlab;heterogeneous networks;k-grams;hinge-loss markov random fields (hl-mrf);multi-relational networks;probabilistic soft logic (psl);sequence mining;social networks},
 abstract={Detecting unsolicited content and the spammers who create it is a long-standing challenge that affects all of us on a daily basis. The recent growth of richly-structured social networks has provided new challenges and opportunities in the spam detection landscape. Motivated by the Tagged.com social network, we develop methods to identify spammers in evolving multi-relational social networks. We model a social network as a time-stamped multi-relational graph where vertices represent users, and edges represent different activities between them. To identify spammer accounts, our approach makes use of structural features, sequence modelling, and collective reasoning. We leverage relational sequence information using k-gram features and probabilistic modelling with a mixture of Markov models. Furthermore, in order to perform collective reasoning and improve the predictive power of a noisy abuse reporting system, we develop a statistical relational model using hinge-loss Markov random fields (HL-MRFs), a class of probabilistic graphical models which are highly scalable. We use Graphlab Create and Probabilistic Soft Logic (PSL) to prototype and experimentally evaluate our solutions on internet-scale data from Tagged.com. Our experiments demonstrate the effectiveness of our approach, and show that models which incorporate the multi-relational nature of the social network significantly gain predictive performance over those that do not.  SOURCE MATERIALS     AVAILABLE FOR DOWNLOAD    Buy this Article    PDF (847KB)     Mp4 (19:06) (253.20MB)      AVAILABLE via FLASH STREAMING SERVICE      Play Stream(19:06)    Buy this Article    APPEARS IN    Artificial Intelligence    Digital Content},
}
@article{Lee2015CharacterizingAA,
  title={Characterizing and automatically detecting crowdturfing in Fiverr and Twitter},
  author={Kyumin Lee and Steve Webb and Hancheng Ge},
  journal={Social Netw. Analys. Mining},
  year={2015},
  volume={5},
  pages={2:1-2:16},
}
@inproceedings{Hu2014Online,
  title={Online social spammer detection},
  author={Hu, Xia and Tang, Jiliang and Liu, Huan},
  booktitle={Twenty-Eighth AAAI Conference on Artificial Intelligence},
  year={2014},
 abstract={The explosive use of social media also makes it a popular platform for malicious users, known as social spammers, to overwhelm normal users with unwanted content. One effective way for social spammer detection is to build a classifier based on content and social network information. However, social spammers are sophisticated and adaptable to game the system with fast evolving content and network patterns. First, social spammers continually change their spamming content patterns to avoid being detected. Second, reflexive reciprocity makes it easier for social spammers to establish social influence and pretend to be normal users by quickly accumulating a large number of âhuman â friends. It is challenging for existing anti-spamming systems based on batch-mode learning to quickly respond to newly emerging patterns for effective social spammer detection. In this paper, we present a general optimization framework to collectively use content and network information for social spammer detection, and provide the solution for efficient online processing. Experimental results on Twitter datasets confirm the effectiveness and efficiency of the proposed framework.},
}

@article{ChenOpinion,
  title={Opinion Spam Detection in Web Forum: A Real Case Study},
  author={Chen, Yu Ren and Chen, Hsin Hsi},
  journal={in Proceedings of the 24th International Conference on World Wide Web.International World Wide Web Conferences Steering Committee},
  year={2015},
 abstract={Opinion spamming refers to the illegal marketing practice which involves delivering commercially advantageous opinions as regular users. In this paper, we conduct a real case study based on a set of internal records of opinion spams leaked from a shady marketing campaign. We explore the characteristics of opinion spams and spammers in a web forum to obtain some insights, including subtlety property of opinion spams, spam post ratio, spammer accounts, first post and replies, submission time of posts, activeness of threads, and collusion among spammers. Then we present features that could be potentially helpful in detecting spam opinions in threads. The results of spam detection on first posts show: (1) spam first posts put more focus on certain topics such as the user experiences' on the promoted items, (2) spam first posts generally use more words and pictures to showcase the promoted items in an attempt to impress people, (3) spam first posts tend to be submitted during work time, and (4) the threads that spam first posts initiate are more active to be placed at striking positions. The spam detection on replies is more challenging. Besides lower spam ratio and less content, replies even do not mention the promoted items. Their major intention is to keep the discussion in a thread alive to attract more attention on it. Submission time of replies, thread activeness, position of replies, and spamicity of first post are more useful than content- based features in spam detection on replies.},
}
@inproceedings{Hu2013Social,
  title={Social spammer detection in microblogging},
  author={Hu, Xia and Tang, Jiliang and Zhang, Yanchao and Liu, Huan},
  booktitle={International Joint Conference on Artificial Intelligence},
  pages={1709-1714},
  year={2013},
 keywords={Influence Evaluation;Micro-Blogging;Page Rank;User Authentication},
 abstract={In resent years,the data mining and analysis of the microblogging has been a hot spot with its vigorous development.In its propagation process, the usersâ ranking is generally arranged in out-degree, which cannot reflect the potential forwarding possibility of micro-blogs.To solve this problem, PageRank algorithm was used to present the algorithm of microblogging propagation node influence evaluation based on user authentication,which enables us effectively predict the future ranking of usersâ nodes and shows some practical value.},
}
@inproceedings{Xu2015RevealingCA,
  title={Revealing, characterizing, and detecting crowdsourcing spammers: A case study in community Q and A},
  author={Aifang Xu and Xiaonan Feng and Ye Tian},
  booktitle={INFOCOM},
  year={2015},
}
@inproceedings{Wang2015DetectingIH,
  title={Detecting Internet Hidden Paid Posters Based on Group and Individual Characteristics},
  author={Xiang Wang and Bin Zhou and Yan Jia and Shasha Li},
  booktitle={WISE},
  year={2015}
}
@inproceedings{Wang2014Man,
  title={Man vs. Machine: Practical Adversarial Detection of Malicious Crowdsourcing Workers},
  author={Wang, Gang and Wang, Tianyi and Zheng, Haitao and Zhao, Ben Y},
  booktitle={The  Usenix Security Symposium},
  year={2014},
 abstract={Recent work in security and systems has embraced the use of machine learning (ML) techniques for identifying misbehavior, email spam and fake (Sybil) users in social networks. However, ML models are typically derived from datasets, and must be periodically retrained. In adversarial environments, attackers can adapt by modifying their behavior or even sabotaging ML models by polluting training data.},
}
@inproceedings{Lee2013Crowdturfers,
  title={Crowdturfers, Campaigns, and Social Media: Tracking and Revealing Crowdsourced Manipulation of Social Media},
  author={Lee, Kyumin and Tamilarasan, Prithivi and Caverlee, James},
  booktitle={ in ICWSM},
  year={2013},
 abstract={Crowdturfing has recently been identified as a sinister counterpart to the enormous positive opportunities of crowdsourcing. Crowdturfers leverage human-powered crowdsourcing platforms to spread malicious URLs in social media, form âastroturf â campaigns, and manipulate search engines, ultimately degrading the quality of online information and threatening the usefulness of these systems. In this paper we present a framework for âpulling back the curtain â on crowdturfers to reveal their underlying ecosystem. Concretely, we analyze the types of malicious tasks and the properties of requesters and workers in crowdsourcing sites such as Microworkers.com, ShortTask.com and Rapidworkers.com, and link these tasks (and their associated workers) on crowdsourcing sites to social media, by monitoring the activities of social media participants. Based on this linkage, we identify the relationship structure connecting these workers in social media, which can reveal the implicit power structure of crowdturfers identified on crowdsourcing sites. We identify three classes of crowdturfers â professional workers, casual workers, and middlemen â and we develop statistical user models to automatically differentiate these workers and regular social media users.},
}

@book{Liu2015Structural,
  title={Structural Analysis of IWA Social Network},
  author={Liu, Wenpeng and Cao, Yanan and Li, Diying and Niu, Wenjia and Tan, Jianlong and Hu, Yue and Guo, Li},
  publisher={Springer Berlin Heidelberg},
  year={2015},
 keywords={Internet water army;Social network;Community detection;Community structure;Sina micro-blog},
 abstract={Internet Water Army (IWA), a special group of online users, has more and more engaged our attention due to the negative effects caused by their irresponsible comments or articles. While most of relate},
}

@inproceedings{Li2013Deceptive,
  title={Deceptive Answer Prediction with User Preference Graph},
  author={Li, Fangtao and Gao, Yang and Zhou, Shuchang and Si, Xiance and Dai, Decheng},
  booktitle={Meeting of the Association for Computational Linguistics},
  pages={1723-1732},
  year={2013},
 abstract={In Community question answering (QA) sites, malicious users may provide deceptive answers to promote their products or services. It is important to identify and filter out these deceptive answers. In this paper, we first solve this problem with the traditional supervised learning methods. Two kinds of features, including textual and contextual features, are investigated for this task. We further propose to exploit the user relationships to identify the deceptive answers, based on the hypothesis that similar users will have similar behaviors to post deceptive or authentic answers. To measure the user similarity, we propose a new user preference graph based on the answer preference expressed by users, such as âhelpful â voting and âbest answer â selection. The user preference graph is incorporated into traditional supervised learning framework with the graph regularization technique. The experiment results demonstrate that the user preference graph can indeed help improve the performance of deceptive answer prediction. 1},
}

@inproceedings{Becchetti2006Link,
  title={Link-Based Characterization and Detection of Web Spam},
  author={Becchetti and Luca and Castillo and Carlos and Donato and Debora and Leonardi and Stefano and Baeza-Yates and Ricardo},
  year={2006},
  abstract={We perform a statistical analysis of a large collection of Web pages, focusing on spam detection. We study several metrics such as degree correlations, number of neighbors, rank propagation through links, TrustRank and others to build several automatic web spam classifiers. This paper presents a study of the performance of each of these classifiers alone, as well as their combined performance. Using this approach we are able to detect 80.4% of the Web spam in our sample, with only 1.1% of false positives.},
}
% old Bencz2006Link
@inproceedings{benczur2006link,
  title={Link-based similarity search to fight web spam},
  author={Bencz{\'u}r, Andr{\'a}s A and Csalog{\'a}ny, K{\'a}roly and Sarl{\'o}s, Tam{\'a}s},
  booktitle={In AIRWEB},
  year={2006},
  organization={Citeseer}
}




@article{Stauber1995Toxic,
  title={Toxic sludge is good for you : lies, damn lies, and the public relations industry},
  author={Stauber, John Clyde and Rampton, Sheldon},
  journal={Journalism $\&$ Mass Communication Educator},
  volume={52},
  number={3},
  pages={314-317},
  year={1995},
 keywords={Book reviews;Public relations;Propaganda;Journalistic ethics},
 abstract={Toxic sludge is good for you : lies, damn lies, and the public relations industry John Stauber and Sheldon Rampton ; introduction by Mark Dowie Common Courage Press, c1995 pbk.},
}

@article{John2010Researching,
  title={Researching Advocacy Groups: Internet Sources for Research about Public Interest Groups and Social Movement Organizations},
  author={John   G.   McNutt},
  journal={Journal of Policy Practice},
  volume={9},
  number={3},
  pages={308-312},
  year={2010},
}

@article{Cho2011Astroturfing,
  title={Astroturfing global warming: It isn’t always greener on the other side of the fence},
  author={Cho, Charles H. and Martens, Martin L. and Kim, Hakkyun and Rodrigue, Michelle},
  journal={Journal of Business Ethics},
  volume={104},
  number={4},
  pages={571-587},
  year={2011},
 keywords={Astroturfing;Business ethics;Climate change;Global warming;Grassroots organizations;Legitimacy;Rhetoric},
 abstract={Astroturf organizations are fake grassroots organizations usually sponsored by large corporations to support any arguments or claims in their favor, or to challenge and deny those against them. They constitute the corporate version of grassroots social movements. Serious ethical and societal concerns underline this astroturfing practice, especially if corporations are successful in influencing public opinion by undertaking a social movement approach. This study is motivated by this particular issue and examines the effectiveness of astroturf organizations in the global warming context, wherein large corporate polluters have an incentive to set up astroturf organizations to undermine the importance of human activities in climate change. We conduct an experiment to determine whether astroturf organizations have an impact on the level of user certainty about the causes of global warming. Results show that people who used astroturf websites became more uncertain about the causes of global warming and humansé¥ role in the phenomenon than people who used grassroots websites. Astroturf organizations are hence successful in promoting business interests over environmental protection. In addition to the multiple business ethics issues it raises, astroturfing poses a significant threat to the legitimacy of the grassroots movement.},
}

@article{Hoggan2010,
  title={"Climate Cover-Up: The Crusade to Deny Global Warming"},
  author={Hoggan, James and Littlemore, Richard},
  journal={Energy $\&$ Environment},
  volume={21},
  number={3},
  pages={363-364},
  year={2010},
 abstract={Climate cover-up : the crusade to deny global warming James Hoggan ; with Richard Littlemore Greystone Books, c2009},
}
@article{Lyon2004Astroturf,
  title={Astroturf: Interest Group Lobbying and Corporate Strategy},
  author={Lyon, Thomas P. and Maxwell, John W.},
  journal={Journal of Economics $\&$ Management Strategy},
  volume={13},
  number={4},
  pages={561-597},
  year={2004},
}
@article{Lau2011Text,
  title={Text mining and probabilistic language modeling for online review spam detection},
  author={Lau, Raymond Y. K. and Liao, S. Y. and Kwok, Chi Wai and Xu, Kaiquan and Xia, Yunqing and Li, Yuefeng},
  journal={Acm Transactions on Management Information Systems},
  volume={2},
  number={4},
  pages={1-30},
  year={2011},
 keywords={Language models;design science;review spam;spam detection;text mining},
 abstract={ABSTRACT In the era of Web 2.0, huge volumes of consumer reviews are posted to the Internet every day. Manual approaches to detecting and analyzing fake reviews (i.e., spam) are not practical due to the problem of information overload. However, the design and development of automated methods of detecting fake reviews is a challenging research problem. The main reason is that fake reviews are specifically composed to mislead readers, so they may appear the same as legitimate reviews (i.e., ham). As a result, discriminatory features that would enable individual reviews to be classified as spam or ham may not be available. Guided by the design science research methodology, the main contribution of this study is the design and instantiation of novel computational models for detecting fake reviews. In particular, a novel text mining model is developed and integrated into a semantic language model for the detection of untruthful reviews. The models are then evaluated based on a real-world dataset collected from amazon.com. The results of our experiments confirm that the proposed models outperform other well-known baseline models in detecting fake reviews. To the best of our knowledge, the work discussed in this article represents the first successful attempt to apply text mining methods and semantic language models to the detection of fake consumer reviews. A managerial implication of our research is that firms can apply our design artifacts to monitor online consumer reviews to develop effective marketing or product design strategies based on genuine consumer feedback posted to the Internet.},
}

@inproceedings{Li2011Learning,
  title={Learning to identify review spam},
  author={Li, Fangtao and Huang, Minlie and Yang, Yi and Zhu, Xiaoyan},
  booktitle={International Joint Conference on Artificial Intelligence},
  pages={2488-2493},
  year={2011},
 keywords={SENTIMENT ANALYSIS;SPAM;PRODUCT REVIEWS;MACHINE LEARNING;MARKETING;Conference Paper},
 abstract={In the past few years, sentiment analysis and opin- ion mining becomes a popular and important task. These studies all assume that their opinion re- sources are real and trustful. However, they may encounter the faked opinion or opinion spam prob- lem. In this paper, we study this issue in the context of our product review mining system. On product review site, people may write faked reviews, called review spam, to promote their products, or defame their competitors' products. It is important to iden- tify and filter out the review spam. Previous work only focuses on some heuristic rules, such as help- fulness voting, or rating deviation, which limits the performance of this task.In this paper, we exploit machine learning meth- ods to identify review spam. Toward the end, we manually build a spam collection from our crawled reviews. We first analyze the effect of various fea- tures in spam identification. We also observe that the review spammer consistently writes spam. This provides us another view to identify review spam: we can identify if the author of the review is spam- mer. Based on this observation, we provide a two- view semi-supervised method, co-training, to ex- ploit the large amount of unlabeled data. The ex- periment results show that our proposed method is effective. Our designed machine learning methods achieve significant improvements in comparison to the heuristic baselines.},
}

@inproceedings{Liu2003Building,
  title={Building Text Classifiers Using Positive and Unlabeled Examples},
  author={Liu, Bing and Dai, Yang and Li, Xiaoli and Lee, Wee Sun and Yu, Philip S.},
  booktitle={Data Mining, IEEE International Conference on},
  pages={179},
  year={2003},
 keywords={Bayes methods;belief networks;pattern classification;support vector machines;text analysis;SVM;positive example;text classifier;unlabeled example;Biomedical engineering},
 abstract={This paper studies the problem of building text classifiers using positive and unlabeled examples. The key feature of this problem is that there is no negative example for learning. Recently, a few techniques for solving this problem were proposed in the literature. These techniques are based on the same idea, which builds a classifier in two steps. Each existing technique uses a different method for each step. In this paper, we first introduce some new methods for the two steps, and perform a comprehensive evaluation of all possible combinations of methods of the two steps. We then propose a more principled approach to solving the problem based on a biased formulation of SVM, and show experimentally that it is more accurate than the existing techniques.},
}

@article{Yang2014Uncovering,
  title={Uncovering social network Sybils in the wild},
  author={Yang, Zhi and Wilson, Christo and Wang, Xiao and Gao, Tingting and Zhao, Ben Y. and Dai, Yafei},
  journal={Acm Transactions on Knowledge Discovery from Data},
  volume={8},
  number={1},
  pages={5-33},
  year={2014},
 keywords={Online social networks;Sybil attacks;measurement;spam;user behavior},
 abstract={Sybil accounts are fake identities created to unfairly increase the power or resources of a single malicious user. Researchers have long known about the existence of Sybil accounts in online communities such as file-sharing systems, but they have not been able to perform large-scale measurements to detect them or measure their activities. In this article, we describe our efforts to detect, characterize, and understand Sybil account activity in the Renren Online Social Network (OSN). We use ground truth provided by Renren Inc. to build measurement-based Sybil detectors and deploy them on Renren to detect more than 100,000 Sybil accounts. Using our full dataset of 650,000 Sybils, we examine several aspects of Sybil behavior. First, we study their link creation behavior and find that contrary to prior conjecture, Sybils in OSNs do not form tight-knit communities. Next, we examine the fine-grained behaviors of Sybils on Renren using clickstream data. Third, we investigate behind-the-scenes collusion between large groups of Sybils. Our results reveal that Sybils with no explicit social ties still act in concert to launch attacks. Finally, we investigate enhanced techniques to identify stealthy Sybils. In summary, our study advances the understanding of Sybil behavior on OSNs and shows that Sybils can effectively avoid existing community-based Sybil detectors. We hope that our results will foster new research on Sybil detection that is based on novel types of Sybil features.},
}

@inproceedings{Ghosh2012Understanding,
  title={Understanding and combating link farming in the twitter social network},
  author={Ghosh, Saptarshi and Viswanath, Bimal and Kooti, Farshad and Sharma, Naveen Kumar and Korlam, Gautam and Benevenuto, Fabricio and Ganguly, Niloy and Gummadi, Krishna Phani},
  booktitle={International Conference on World Wide Web},
  pages={56-61},
  year={2012},
 keywords={collusionrank;link farming;pagerank;spam;twitter},
 abstract={Recently, Twitter has emerged as a popular platform for discovering real-time information on the Web, such as news stories and peopleâs reaction tothem. Like theWeb, Twitter has become a target for link farming, where users, especially spammers, try to acquire large numbers of follower links in the social network. Acquiring followers not only increases the size of a userâs direct audience, but also contributes to the perceived influence of the user, which in turn impacts the ranking of the userâs tweets by search engines. In this paper, we first investigate link farming in the Twitter network and then explore mechanisms to discourage the activity. To this end, we conducted a detailed analysis of links acquired by over 40,000 spammer accounts suspended by Twitter. We find that link farming is wide spread and that a majority of spammers â links are farmed from a small fraction of Twitter users, the social capitalists, who are themselves seeking to amass social capital and links by following back anyone who follows them. Our findings shed light on the social dynamics that are at the root of the link farming problem in Twitter network and they have important implications for future designs of link spam defenses. In particular, we show that a simple user ranking scheme that penalizes users for connecting to spammers can effectively address the problem by disincentivizing users from linking with other users simply to gain influence. Categories andSubject Descriptors H.3.5 [Online Information Services]: Web-based services;},
}

@inproceedings{Jiang2015A,
  title={A General Suspiciousness Metric for Dense Blocks in Multimodal Data},
  author={Jiang, Meng and Beutel, Alex and Cui, Peng and Hooi, Bryan},
  booktitle={International Conference on Data Mining},
  pages={781-786},
  year={2015},
 keywords={Data mining;Facebook;IP networks;Inspection;Measurement;Tensile stress;Twitter;dense block;multimodal data;suspicious behavior},
 abstract={Which seems more suspicious: 5,000 tweets from 200 users on 5 IP addresses, or 10,000 tweets from 500 users on 500 IP addresses but all with the same trending topic and all in 10 minutes? The literature has many methods that try to find dense blocks in matrices, and, recently, tensors, but no method gives a principled way to score the suspiciouness of dense blocks with different numbers of modes and rank them to draw human attention accordingly. Dense blocks are worth inspecting, typically indicating fraud, emerging trends, or some other noteworthy deviation from the usual. Our main contribution is that we show how to unify these methods and how to give a principled answer to questions like the above. Specifically, (a) we give a list of axioms that any metric of suspicousness should satisfy, (b) we propose an intuitive, principled metric that satisfies the axioms, and is fast to compute, (c) we propose CROSSSPOT, an algorithm to spot dense regions, and sort them in importance ("suspiciousness") order. Finally, we apply CROSSSPOT to real data, where it improves the F1 score over previous techniques by 68% and finds retweet-boosting in a real social dataset spanning 0.3 billion posts.},
}

@inproceedings{Lee2010Uncovering,
  title={Uncovering social spammers: social honeypots + machine learning},
  author={Lee, Kyumin and Caverlee, James and Webb, Steve},
  booktitle={Proceeding of the  International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, Geneva, Switzerland, July},
  pages={435-442},
  year={2010},
 keywords={social honeypots;social media;spam},
 abstract={CiteSeerX - Scientific documents that cite the following paper: Uncovering social spammers: Social honeypots + machine learning},
}

@inproceedings{Hu2014Social,
  title={Social Spammer Detection with Sentiment Information},
  author={Hu, Xia and Tang, Jiliang and Gao, Huiji and Liu, Huan},
  booktitle={IEEE International Conference on Data Mining},
  pages={180-189},
  year={2014},
 keywords={information dissemination;optimisation;psychology;social networking (online);unsolicited e-mail;information dissemination;information sharing;online social media;optimization formulation;psychological findings},
 abstract={Social media is a popular platform for spammers to unfairly overwhelm normal users with unwanted or fake content via social networking. The spammers significantly hinder the use of social media systems for effective information dissemination and sharing. Different from the spammers in traditional platforms such as email and the Web, spammers in social media can easily connect with each other, sometimes without mutual consent. They collude with each other to imitate normal users by quickly accumulating a large number of "human" friends. In addition, content information in social media is noisy and unstructured. It is infeasible to directly apply traditional spammer detection methods in social media. Understanding and detecting deception has been extensively studied in traditional sociology and social sciences. Motivated by psychological findings in physical world, we investigate whether sentiment analysis can help spammer detection in online social media. In particular, we first conduct an exploratory study to analyze the sentiment differences between spammers and normal users, and then present an optimization formulation that incorporates sentiment information into a novel social spammer detection framework. Experimental results on real-world social media datasets show the superior performance of the proposed framework by harnessing sentiment analysis for social spammer detection.},
}

@inproceedings{Ratkiewicz2011Detecting,
  title={Detecting and Tracking Political Abuse in Social Media.},
  author={Ratkiewicz, Jacob and Conover, Michael and Meiss, Mark and GonÃ§alves, Bruno and Flammini, Alessandro and Menczer, Filippo},
  booktitle={International Conference on Weblogs and Social Media, Barcelona, Catalonia, Spain, July},
  year={2011},
 abstract={We study astroturf political campaigns on microblogging platforms: politically-motivated individuals and organiza-tions that use multiple centrally-controlled accounts to create the appearance of widespread support for a candidate or opin-ion. We describe a machine learning framework that com-bines topological, content-based and crowdsourced features of information diffusion networks on Twitter to detect the early stages of viral spreading of political misinformation. We present promising preliminary results with better than 96% accuracy in the detection of astroturf content in the run-up to the 2010 U.S. midterm elections. 1},
}

%2021-01-29
@article{dong2018unsupervised,
  title={An unsupervised topic-sentiment joint probabilistic model for detecting deceptive reviews},
  author={Dong, Lu-yu and Ji, Shu-juan and Zhang, Chun-jin and Zhang, Qi and Chiu, DicksonK W and Qiu, Li-qing and Li, Da},
  journal={Expert Systems with Applications},
  volume={114},
  pages={210--223},
  year={2018},
  publisher={Elsevier}
}

@article{liu2018unified,
  title={A unified framework for detecting author spamicity by modeling review deviation},
  author={Liu, Yuanchao and Pang, Bo},
  journal={Expert Systems with Applications},
  volume={112},
  pages={148--155},
  year={2018},
  publisher={Elsevier}
}

@article{zhang2018dri,
  title={DRI-RCNN: An approach to deceptive review identification using recurrent convolutional neural network},
  author={Zhang, Wen and Du, Yuhang and Yoshida, Taketoshi and Wang, Qing},
  journal={Information Processing \& Management},
  volume={54},
  number={4},
  pages={576--592},
  year={2018},
  publisher={Elsevier}
}

@article{you2020integrating,
  title={Integrating aspect analysis and local outlier factor for intelligent review spam detection},
  author={You, Lan and Peng, Qingxi and Xiong, Zenggang and He, Du and Qiu, Meikang and Zhang, Xuemin},
  journal={Future Generation Computer Systems},
  volume={102},
  pages={163--172},
  year={2020},
  publisher={Elsevier}
}

@article{barushka2020spam,
  title={Spam detection on social networks using cost-sensitive feature selection and ensemble-based regularized deep neural networks},
  author={Barushka, Aliaksandr and Hajek, Petr},
  journal={Neural Computing and Applications},
  volume={32},
  number={9},
  pages={4239--4257},
  year={2020},
  publisher={Springer}
}

@article{dhingra2019spam,
  title={Spam analysis of big reviews dataset using Fuzzy Ranking Evaluation Algorithm and Hadoop},
  author={Dhingra, Komal and Yadav, Sumit Kr},
  journal={International Journal of Machine Learning and Cybernetics},
  volume={10},
  number={8},
  pages={2143--2162},
  year={2019},
  publisher={Springer}
}

@article{liu2019opinion,
  title={Opinion spam detection by incorporating multimodal embedded representation into a probabilistic review graph},
  author={Liu, Yuanchao and Pang, Bo and Wang, Xiaolong},
  journal={Neurocomputing},
  volume={366},
  pages={276--283},
  year={2019},
  publisher={Elsevier}
}

@article{dong2020opinion,
  title={Opinion fraud detection via neural autoencoder decision forest},
  author={Dong, Manqing and Yao, Lina and Wang, Xianzhi and Benatallah, Boualem and Huang, Chaoran and Ning, Xiaodong},
  journal={Pattern Recognition Letters},
  volume={132},
  pages={21--29},
  year={2020},
  publisher={Elsevier}
}

@article{li2017document,
  title={Document representation and feature combination for deceptive spam review detection},
  author={Li, Luyang and Qin, Bing and Ren, Wenjing and Liu, Ting},
  journal={Neurocomputing},
  volume={254},
  pages={33--41},
  year={2017},
  publisher={Elsevier}
}

@inproceedings{aghakhani2018detecting,
  title={Detecting deceptive reviews using generative adversarial networks},
  author={Aghakhani, Hojjat and Machiry, Aravind and Nilizadeh, Shirin and Kruegel, Christopher and Vigna, Giovanni},
  booktitle={2018 IEEE Security and Privacy Workshops (SPW)},
  pages={89--95},
  year={2018},
  organization={IEEE}
}

@inproceedings{li2014towards,
  title={Towards a general rule for identifying deceptive opinion spam},
  author={Li, Jiwei and Ott, Myle and Cardie, Claire and Hovy, Eduard},
  booktitle={Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages={1566--1576},
  year={2014}
}

@article{ren2019learning,
  title={Learning to detect deceptive opinion spam: A survey},
  author={Ren, Yafeng and Ji, Donghong},
  journal={IEEE Access},
  volume={7},
  pages={42934--42945},
  year={2019},
  publisher={IEEE}
}


@inproceedings{liu2016statistical,
  title={Statistical detection of online drifting twitter spam},
  author={Liu, Shigang and Zhang, Jun and Xiang, Yang},
  booktitle={Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security},
  pages={1--10},
  year={2016}
}

@article{shehnepoor2017netspam,
  title={NetSpam: A network-based spam detection framework for reviews in online social media},
  author={Shehnepoor, Saeedreza and Salehi, Mostafa and Farahbakhsh, Reza and Crespi, Noel},
  journal={IEEE Transactions on Information Forensics and Security},
  volume={12},
  number={7},
  pages={1585--1595},
  year={2017},
  publisher={IEEE}
}

@article{noekhah2020opinion,
  title={Opinion spam detection: Using multi-iterative graph-based model},
  author={Noekhah, Shirin and binti Salim, Naomie and Zakaria, Nor Hawaniah},
  journal={Information Processing \& Management},
  volume={57},
  number={1},
  pages={102140},
  year={2020},
  publisher={Elsevier}
}

@article{xia2017our,
  title={" Our Privacy Needs to be Protected at All Costs" Crowd Workers' Privacy Experiences on Amazon Mechanical Turk},
  author={Xia, Huichuan and Wang, Yang and Huang, Yun and Shah, Anuj},
  journal={Proceedings of the ACM on Human-Computer Interaction},
  volume={1},
  number={CSCW},
  pages={1--22},
  year={2017},
  publisher={ACM New York, NY, USA}
}

@article{wu2018twitter,
  title={Twitter spam detection: Survey of new approaches and comparative study},
  author={Wu, Tingmin and Wen, Sheng and Xiang, Yang and Zhou, Wanlei},
  journal={Computers \& Security},
  volume={76},
  pages={265--284},
  year={2018},
  publisher={Elsevier}
}

@inproceedings{wang2020wefend,
  title={Weak supervision for fake news detection via reinforcement learning},
  author={Wang, Yaqing and Yang, Weifeng and Ma, Fenglong and Xu, Jin and Zhong, Bin and Deng, Qiang and Gao, Jing},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={34},
  number={01},
  pages={516--523},
  year={2020}
}